E-Dissertationen

Toxicogenomic and structural determinants of cytochrome P450-mediated insecticide detoxification and selectivity in bee pollinators

2026-10-06T00:00:00Z

Toxicogenomic and structural determinants of cytochrome P450-mediated insecticide detoxification and selectivity in bee pollinators Xiao, Xingzhi Bee pollinators provide essential ecosystem services but face multiple threats, including exposure to insecticides. Current pesticide risk assessments rely on empirical toxicity testing of a limited number of bee species (primarily Apis mellifera), which inadequately predict sensitivity across more than 20,000 bee species globally. Understanding the molecular mechanisms underlying differential pesticide sensitivity in bees is essential for effective pollinator protection. Cytochrome P450 monooxygenases (P450s), particularly the CYP9Q subfamily in honey bees (A. mellifera), have emerged as key determinants of insecticide selectivity through their capacity to metabolize xenobiotics, with recent phylogenomic analyses demonstrating conservation of this detoxification capacity across major bee families. However, the conservation of these detoxification mechanisms across bee species, the molecular basis of substrate specificity, and the complexity of P450-mediated metabolism remain incompletely understood. This cumulative thesis applied toxicogenomics approaches to characterize the functional properties of bee P450 enzymes and their role in differential pesticide sensitivity.
Chapter 2 investigated the conservation of flupyradifurone (FPF) metabolism across eight bee species, including stingless bees (Tribe: Meliponini). Phylogenetic analysis identified functional orthologs of the honey bee CYP6AQ1 in all examined species, showing greater than 61% sequence identity. Heterologous expression in insect cells revealed that all orthologs exhibited similar coumarin substrate profiles and metabolized FPF via hydroxylation, demonstrating conserved detoxification capacity across phylogenetically diverse bee taxa.
Chapter 3 elucidated the molecular mechanisms underlying coumaphos tolerance in western honey bees (A. mellifera). Synergist bioassays and biochemical characterization revealed that CYP9Q2 plays a pivotal role in coumaphos detoxification through rapid hydroxylation of the active metabolite coumaphos-oxon. Importantly, the study demonstrated suicide substrate properties of coumaphos, whereby desulfuration during P450-mediated activation leads to enzyme inactivation, revealing a complex balance between metabolic activation and detoxification. Transgenic Drosophila ectopically expressing CYP9Q2 and CYP9Q3 exhibited significantly increased tolerance to both coumaphos and coumaphos-oxon, functionally validating the role of these P450 enzymes in coumaphos selectivity.
Chapter 4 employed site-directed mutagenesis to identify structural determinants of CYP9Q3 substrate specificity. Subfamily-discriminating position analysis and structural modeling identified nine residues differentiating CYP9Q-type P450s from other CYP3 clan members. Alanine-scanning mutagenesis across substrate recognition site 4 (SRS4) revealed that the FD dipeptide (Phe308-Asp309) is essential for CYP9Q3 catalytic activity. Reciprocal indel experiments with Megachile rotundata CYP9DMs, which lack this dipeptide, confirmed that while FD deletion abolished CYP9Q3 activity, FD insertion alone was insufficient to confer CYP9Q-like detoxification capacity to CYP9DM1, indicating that multiple structural features collectively determine enzyme function.
This thesis advances the molecular understanding of insecticide metabolism in bees and demonstrates the role of P450 enzymes in mediating species-specific sensitivity. The findings provide a mechanistic basis for predicting pesticide-bee interactions, inform risk assessment strategies, and support the rational development of bee-selective insecticides that leverage or circumvent specific metabolic pathways.

Holistic Modeling Approaches for Supporting Sustainability Transitions

2026-10-05T00:00:00Z

Holistic Modeling Approaches for Supporting Sustainability Transitions Whitney, Cory William Agricultural and environmental decisions in agroecological systems are made under profound uncertainty, with complex trade-offs between productivity, ecological integrity and human well-being. This cumulative habilitation thesis brings together 32 peer-reviewed studies that develop and apply holistic modeling approaches to support such decisions and, more broadly, sustainability transitions.
The first part advances decision analytical methods in agroecology. Structured decision models, Bayesian networks and Monte Carlo simulations, informed by expert-elicited probabilities, are used to evaluate interventions under incomplete information. Applications span agroforestry adoption and carbon offset schemes, horticultural management strategies, water resource management, flood-based farming systems, agricultural policy and household nutrition, and climate change adaptation and mitigation, with case studies in Africa, Asia, Latin America and Europe. These models identify key trade-offs, risks and leverage points, and value-of-information analyses show where further measurements would most improve decisions.
The second part advances methods in transdisciplinary research. It integrates participatory approaches, stakeholder engagement, local and Indigenous knowledge, and holistic sustainability assessment frameworks to co-create context-specific solutions and to bridge science, policy and practice.
Together, the studies show that combining probabilistic modeling with participatory processes produces decision support that is both scientifically rigorous and practically relevant. The thesis concludes with implications for policy and with future research directions, including hybrid models that combine expert knowledge with emerging data, multi-criteria trade-off assessment, and the scaling of agroecological practices.

Regulatory Networks Underlying Plant Nutrient Status

2026-10-05T00:00:00Z

Regulatory Networks Underlying Plant Nutrient Status Mihiret, Yeshambel Emewodih Plants require 14 mineral nutrients for growth and development. Nutrient imbalances remain major constraints on agricultural productivity. Conventional approaches for diagnosing plant nutritional status often fail to identify the primary limiting element, particularly in acidic soils where multiple deficiencies and toxicities co-occur. Non-invasive tools estimate nutrient status indirectly and are affected by other stresses, leading to misdiagnosis, excessive fertilization, and environmental harm. Thus, precise and element-specific diagnostic strategies are required for sustainable crop production. At the molecular level, nutrient homeostasis is tightly regulated by signaling networks. In this context, inositol pyrophosphates (PP-InsPs) are signaling molecules regulating phosphate signaling and diverse processes, while pyrophosphate (PPi), a by-product of biosynthetic reactions, plays key roles in cellular metabolism. Despite their importance, the link between PP-InsPs and PPi homeostasis has remained poorly understood.
In this thesis, integrative ionomic, transcriptomic, and qPCR approaches were used to identify molecular sentinels that respond specifically to distinct nutrient imbalance conditions in barley. In addition, co-regulated genes were identified, revealing coordinated nutrient interactions. A set of reference genes with stable expression across treatments is also defined, providing a valuable resource for future qPCR studies. These findings demonstrate that molecular sentinels have the potential to overcome key limitations of conventional nutrient analyses and enable early, precise diagnosis of nutritional disorders before visible symptoms or yield losses occur.
This thesis also uncovers a previously unrecognized role of PP-InsPs in regulating PPi homeostasis in plants. Genetic and biochemical studies in Arabidopsis show that canonical PPi-hydrolyzing systems are unlikely to be involved in InsP/PP-InsP metabolism. In contrast, disruption of InsP/PP-InsP biosynthetic enzymes causes marked PPi accumulation accompanied by extensive remodeling of InsP/PP-InsP profiles, adenylate pools, and the plant nutriome. Complementation restores PPi levels, nutrient balance, and shoot growth. Additionally, high PPi exacerbates shoot growth defects, and restoration of PPi levels with the kinase domain of VIH2 suggests that the kinase activity of VIH2 is important for PPi control. Biochemical analyses showed that PPi is not a phosphoryl donor for PP-InsP synthesis, but activates InsP/PP-InsP kinases, and that PP-InsPs stimulate ScIPP1. In addition, sulfur homeostasis is identified as a previously unrecognized macronutrient regulated by PP-InsPs.
In conclusion, this thesis establishes molecular sentinels as a potentially precise diagnostic indicators of plant nutritional status and positions PP-InsPs as central integrators of nutrient signaling, PPi metabolism, and metabolic homeostasis, offering new avenues to improve nutrient use efficiency and support sustainable agriculture.; Pflanzen benötigen 14 mineralische Nährstoffe für Wachstum und Entwicklung. Nährstoffungleichgewichte stellen nach wie vor eine der größten Einschränkungen der landwirtschaftlichen Produktivität dar. Konventionelle Verfahren zur Bestimmung des Ernährungszustands von Pflanzen sind häufig nicht in der Lage, das primär limitierende Element eindeutig zu identifizieren, insbesondere in sauren Böden, in denen mehrere Nährstoffmängel und Toxizitäten gleichzeitig auftreten. Nicht-invasive Methoden schätzen den Nährstoffstatus lediglich indirekt ab und werden durch andere Stressfaktoren beeinflusst, was zu Fehldiagnosen, übermäßiger Düngung und Umweltbelastungen führen kann. Daher sind präzise und elementspezifische Diagnoseverfahren für eine nachhaltige Pflanzenproduktion erforderlich. Auf molekularer Ebene wird die Nährstoffhomöostase durch eng vernetzte Signalnetzwerke reguliert. In diesem Zusammenhang fungieren Inositolpyrophosphate (PP-InsPs) als Signalmoleküle, die die Phosphatsignalübertragung sowie zahlreiche weitere Prozesse regulieren, während Pyrophosphat (PPi), ein Nebenprodukt biosynthetischer Reaktionen, eine zentrale Rolle im zellulären Stoffwechsel spielt. Trotz ihrer Bedeutung ist der Zusammenhang zwischen PP-InsPs und der Homöostase von PPi bislang nur unzureichend verstanden.
In dieser Dissertation wurden integrative ionomische, transkriptomische und qPCR-basierte Ansätze eingesetzt, um molekulare Sentinelmarker zu identifizieren, die spezifisch auf unterschiedliche Bedingungen von Nährstoffungleichgewichten in Gerste reagieren. Darüber hinaus wurden ko-regulierte Gene identifiziert, die koordinierte Wechselwirkungen zwischen verschiedenen Nährstoffen aufzeigen. Zudem wurde ein Satz von Referenzgenen mit stabiler Expression über alle Behandlungen hinweg definiert, der eine wertvolle Grundlage für zukünftige qPCR-Studien darstellt. Diese Ergebnisse zeigen, dass molekulare Sentinelmarker das Potenzial besitzen, wesentliche Einschränkungen konventioneller Nährstoffanalysen zu überwinden und eine frühzeitige sowie präzise Diagnose von Ernährungsstörungen zu ermöglichen, bevor sichtbare Symptome oder Ertragseinbußen auftreten.
Diese Dissertation zeigt zudem eine bislang unbekannte Funktion von PP-InsPs bei der Regulation der PPi-Homöostase in Pflanzen auf. Genetische und biochemische Untersuchungen in Arabidopsis belegen, dass die kanonischen PPi-hydrolysierenden Systeme vermutlich nicht am InsP-/PP-InsP-Stoffwechsel beteiligt sind. Dagegen führt die Störung von Enzymen der InsP-/PP-InsP-Biosynthese zu einer deutlichen Akkumulation von PPi, die mit einer umfassenden Umgestaltung der InsP-/PP-InsP-Profile, der Adenylatpools sowie des pflanzlichen Nährstoffprofils (Nutriom) einhergeht. Die genetische Komplementation stellt sowohl die PPi-Gehalte als auch das Nährstoffgleichgewicht und das Sprosswachstum wieder her. Darüber hinaus verstärken erhöhte PPi-Konzentrationen Defekte des Sprosswachstums, während die Wiederherstellung der PPi-Gehalte durch die Kinasedomäne von VIH2 darauf hindeutet, dass die Kinaseaktivität von VIH2 für die Regulation von PPi von entscheidender Bedeutung ist. Biochemische Analysen zeigten, dass PPi nicht als Phosphorylgruppendonor für die Synthese von PP-InsPs dient, sondern InsP-/PP-InsP-Kinasen aktiviert und dass PP-InsPs ScIPP1 stimulieren. Darüber hinaus wird die Schwefelhomöostase als ein bislang nicht erkannter Makronährstoff-regulierter Prozess identifiziert, der durch PP-InsPs kontrolliert wird.
Zusammenfassend etabliert diese Dissertation molekulare Sentinelmarker als potenziell präzise diagnostische Indikatoren des pflanzlichen Ernährungszustands und positioniert PP-InsPs als zentrale Integratoren der Nährstoffsignalübertragung, des PPi-Stoffwechsels und der metabolischen Homöostase. Damit eröffnet sie neue Perspektiven zur Verbesserung der Nährstoffnutzungseffizienz und zur Förderung einer nachhaltigen Landwirtschaft.

Neural Representations for Simultaneous Localization and Mapping

2026-10-05T00:00:00Z

Neural Representations for Simultaneous Localization and Mapping Pan, Yue Autonomous robots need to know where they are and what surrounds them to operate reliably in the real world. This challenge of reconstructing the environment and localizing the robot within it is known as simultaneous localization and mapping (SLAM). Both tasks are grounded in a scene representation, an internal model of the robot's surroundings that also supports downstream applications such as navigation and simulation. This thesis addresses what form this representation should take and how it can be built and continuously updated from sensor measurements.
Conventional scene representations such as point clouds, voxels, surfels, and meshes store geometry in explicit and discretized data structures, and have long formed the basis of modern robotic systems. However, their fixed-resolution discretization introduces information loss during fusion, and robots often need to maintain multiple redundant representations for different downstream tasks. Recent learning-based scene representations model scenes in a continuous and compact form, optimizing learnable parameters via differentiable functions to fit sensor observations. Yet many such methods are confined to small-scale offline operation, prone to catastrophic forgetting during long-term deployment, unable to accommodate loop closure corrections without remapping, and limited to a single sensor modality. To address these challenges, this thesis proposes a novel point-based implicit neural scene representation. It consists of sparse neural points, each with a local coordinate frame and an optimizable latent feature, decoded by globally shared neural networks into scene geometry, appearance, or object-level structure on demand. The representation is locally grounded, continuously learnable, scalable, and versatile.
The thesis makes four main contributions that progressively extend this representation. The first contribution presents, to the best of our knowledge, the first full-fledged LiDAR SLAM system built entirely on a neural implicit representation. It integrates correspondence-free odometry, incremental map learning, and loop closure into a full pipeline, where loop closure corrections elastically deform the neural point map without remapping. The system runs at sensor frame rate and achieves state-of-the-art localization and surface reconstruction accuracy on established benchmarks while requiring less map memory than previous scene representations. While this system captures scene geometry, it cannot model appearance. The second contribution extends the representation to LiDAR-visual SLAM by jointly modeling a signed distance field and a Gaussian splatting radiance field in the same neural point map, where geometry and appearance mutually improve each other through a consistency loss. The system scales to kilometers-long trajectories while maintaining a globally consistent and compact map, achieving state-of-the-art photorealistic rendering and geometric reconstruction accuracy. Both contributions, however, treat the scene as a whole without modeling individual objects or their category specific structure. Thus, the third contribution addresses object-level modeling in cluttered horticultural environments, where occlusion makes per-instance shape estimation particularly challenging. It embeds learned shape priors into an occlusion-aware differentiable rendering pipeline that jointly estimates complete fruit geometry and pose from partial RGB-D observations, outperforming existing baselines in both shape completion and pose accuracy. The resulting models have been integrated into downstream robotic systems for autonomous fruit harvesting and safe leaf manipulation. All three contributions, however, operate within a single mapping session. To overcome this limitation, the fourth contribution addresses multi-session map fusion by casting multi-view point cloud registration as a single-stage 3D generation problem, bypassing exhaustive pairwise matching. A flow matching model with rigidity-enforcing sampling directly generates registered point clouds in a canonical frame from unposed multi-view inputs. Trained on large-scale cross-domain data, the model achieves top registration accuracy with the shortest runtime and scales favorably with view count. It also generalizes zero-shot across scene scales, sensor modalities, and low-overlap conditions, enabling downstream tasks such as offline SLAM and map merging.
Together, these contributions establish neural point-based representations as a practical and unified foundation for SLAM and downstream tasks spanning geometry, appearance, object-level structure, and multi-view fusion. All methods achieve state-of-the-art results on established benchmarks, have been published in peer-reviewed venues, and are publicly available as open-source software.